Addressing Single and Multiple Bad Data in the Modern PMU-based Power System State Estimation

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Abstract

Detection and analysis of bad data is an important sector of the static state estimation. This paper addresses single and multiple bad data in the modern phasor measurement unit (PMU)-based power system static state estimations. To accomplish this objective, available approaches in the PMU-based state estimation are overviewed, and their advantages and disadvantages are briefly explained. The largest normalized residual test is used to identify bad data. Then, phasor measurements are added by post-processing step in the state estimation. The proposed algorithms of phasor measurements utilization in state estimation can detect and identify single and multiple bad data in redundant and critical measurements. To validate simulations, IEEE 30 bus system are implemented in PowerFactory and Matlab is used to solve proposed state estimation using postprocessing of PMUs and mixed methods. Bad data is generated manually and added in PMU and conventional measurements profile. Finally, the location and analyze of bad data are available by the result of largest normalized residual test.
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Details

Detection and analysis of bad data is an important sector of the static state estimation. This paper addresses single and multiple bad data in the modern phasor measurement unit (PMU)-based power system static state estimations. To accomplish this objective, available approaches in the PMU-based state estimation are overviewed, and their advantages and disadvantages are briefly explained. The largest normalized residual test is used to identify bad data. Then, phasor measurements are added by post-processing step in the state estimation. The proposed algorithms of phasor measurements utilization in state estimation can detect and identify single and multiple bad data in redundant and critical measurements. To validate simulations, IEEE 30 bus system are implemented in PowerFactory and Matlab is used to solve proposed state estimation using postprocessing of PMUs and mixed methods. Bad data is generated manually and added in PMU and conventional measurements profile. Finally, the location and analyze of bad data are available by the result of largest normalized residual test.
Original languageEnglish
Title of host publicationProceedings of 2017 52nd International Universities Power Engineering Conference (UPEC 2017)
Number of pages6
PublisherIEEE Press
Publication dateAug 2017
ISBN (Electronic)978-1-5386-2344-2
DOI
StatePublished - Aug 2017
Publication categoryResearch
Peer-reviewedYes
Event2017 52nd International Universities Power Engineering Conference (UPEC) - Heraklion, Greece
Duration: 28 Aug 201731 Aug 2017

Conference

Conference2017 52nd International Universities Power Engineering Conference (UPEC)
LandGreece
ByHeraklion
Periode28/08/201731/08/2017

    Research areas

  • Bad Data, Critical Measurement, Largest Normalized Residual, Phase Measurement unit, State Estimation

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